The term “Biometrics” is derived from “bio'” which means life, and “metrics”, which means measurement. Biometrics is developing for use in various technologies, including the unique identification and recognition of people. The technology at its current juncture is widely used in security and surveillance systems.

Biometrics also has the possibility of converging with other technologies and scientific studies. For example, behavioral biometrics can help improve newly evolving computer-human interfaces like voice control, gesture, and brainwave control for computer devices. Similarly, morphological and biological biometrics can be beneficial in the study of human evolution. Biological biometrics like DNA recognition combined with different medical and biomedical datasets can aid in the study of genetic diseases and even identify specific racial characteristics.

Behavioral biometrics aid in studying physical (bodily-kinesthetic) intelligence to improve and advance sports and military training. Biometric datasets can be especially in right-brain-education-system training in all eight types of human intelligence. Those comprise the following: verbal-linguistic, logical-mathematical, spatial, musical, bodily-kinesthetic, interpersonal, intrapersonal, and naturalistic. The biometric datasets can be helpful in the early identification of natural talents and improvements in training in specific human intelligence areas.

执法，国防和情报机构已经使用了生物识别数据。印度已成功展示了生物识别数据与人口统计数据的结合如何发布政府控制的ID和AADHAR卡。Aadhar是印度世界上最广泛的生物识别系统，该系统正式和非正式地与银行，金融，所得税，医疗和执法系统融合在一起。甚至有可能将Aadhar数据与土地银行数据集成以遏制Benami Properties的形式。印度政府拥有超过12.8亿印度人的生物识别数据（视网膜，所有十个手指的指纹和面部数据），该数据正式用于实施政府控制社会福利计划。它可用于控制犯罪，执法，反恐，人口监测，识别邻国非法移民，甚至跟踪非法金融交易和洗钱。这可以通过数字化各种公共和私人服务，并将Aadhar（生物特征识别）与各种公共和私人数据集相结合。

While security and surveillance applications are confined to biometric identification and recognition, biometric data can have many applications when combined with big data (different public and private datasets), artificial intelligence, machine learning, the internet of things, and computer-human interfaces.

The very first step in biometrics is the collection of biometric data. For this, there are different types of biometric sensors. These sensors are usually designed as edge devices, and more sophisticated sensors are cloud-controlled, involving a high level of encryption. Let us learn about biometric systems, various biometric sensors, and their types.

什么是生物识别传感器？
生物识别传感器是将生物特征性状转换为电信号的传感器。这些特征包括指纹，虹膜，静脉图案，语音，面部，DNA等，这些特征是根据软件定义的模板对数字化的。后来使用相同的模板进行比较和对人的唯一识别/识别。传感器的工作可能是基于一个或多个物理量的测量，例如光强度，电容，温度，图像等。在安全/监视系统中，生物识别传感器用作身份技术。除了其他传统的访问系统（例如引脚代码或密码），它还确保了精确性，使该系统万无一失。新的安全系统毫不犹豫地仅依靠生物识别身份验证，消除了记住密码或携带安全令牌的需求。

生物识别传感器类型
Biometrics is broadly divided into three categories as follows.

Biological biometrics: This involves biological measurements at the genetic or molecular level, and DNA sequencing is a biological biometric system. Biological biometrics require the sampling of DNA from blood or bodily fluids. This cannot be used for security or authentication systems but has other practical applications like DNA matching, genetic diseases, and microbiological studies.

Morphological biometrics: This involves the measurement of physical traits and body structures. The security and authentication systems are usually based on one or the other morphological biometrics. This includes fingerprint mapping, iris scan, face recognition, finger geometry recognition, vein recognition, hand geometry, ear recognition, and odor recognition.

行为生物识别技术：这涉及对人独有的行为标识符的测量。这些系统并不常见，但保留用于特殊应用。这包括签名识别，语音生物识别技术，步态生物识别技术，击键识别和手势识别。

安全和监视系统的生物识别技术
Security systems use biometrics for authentication, while surveillance systems use biometrics for identification. The security systems usually have reference biometric data stored locally, i.e., in the edge device, or the device might not be connected to any internet network. The data may have stored templates for the identification of several persons. When a person requests access, a new sample of biometric data is collected by the scanner and compared with stored templates to determine if the person is authorized or not. The optical fingerprint scanners are the most widely used biometric security systems. These are low-cost and have applications ranging from standalone embedded devices to smartphones and computers. Multispectral fingerprint scanners are better than optical scanners but are costlier. More sophisticated biometric security systems may involve additional measurements like iris scanning, face recognition, finger geometry, hand geometry, or vein pattern recognition. Iris scanning along with hand geometry are the most preferred high-security systems.

The surveillance systems are intended for identification. Face recognition is the most viable surveillance system, and these systems usually do not have direct interaction or interface with persons to be identified. Biometric surveillance systems are always cloud-based, with biometric data cryptographically sent by edge devices over the network.

How do biometric security sensors work?
A biometric security system is designed for authentication. It involves three key stages – enrollment, storage, and comparison. In enrollment, the biometric data of valid users is entered into the device along with an identification key or number. For example, a fingerprint sensor may collect the fingerprint template of a valid user and assign it a unique identification number. This biometric data is stored in memory according to a software-defined template locally or on a centralized database. With the biometric identity of valid users stored in the device, whenever a user attempts to access the system, a new biometric sample is collected by the biometric scanner and compared with the stored templates. The matching with stored templates determines whether user identity is rejected or validated.

Many biometric security systems have a different approach. The biometric data is stored on a smart card instead of a local device or a centralized server. The security system is designed to validate the biometric data stored in the security token with the live sample collected from its user.

生物识别测量
There are three types of biometric measurements – biological, morphological, and behavioral. Different biometric measurements are discussed below.

生物标识符：
DNA recognition: This involves the collection of DNA samples in the form of blood or bodily fluids. The DNA is sequenced and stored in a similar format. A person’s DNA matches 99.7% with her biological parents, and the rest 0.3% is a variable repetitive code. The redundant code is unique to a person and is used for genetic fingerprinting.

形态学标识符：
Face recognition: This involves capturing a digital image of the face through pictures, video, or real-time streams. The digital image is compared against a face recognition pattern which is a mapping of various facial features.

Fingerprint mapping: Fingerprints are unique identifiers. Fingerprints are scanned using optical, ultrasonic, or capacitive scanners and stored in a predefined template. The new scans are compared with already stored fingerprint identifications to match an identity.

手指几何识别：在该系统中，手指的长度，宽度，区域和厚度被用作唯一标识符。

Hand geometry: This uses physical characteristics of the entire palm, including features of fingers, to identify a person.

Retina recognition: The blood vessels pattern of the retina are unique to a person. The Iris is scanned using visible or infrared light, and the retinal patterns are stored according to software-defined markers. The new scans are then compared with the stored retinal pattern for biometric authentication or identification.

Ear recognition: This uses the structure of the ear as an identifier. The shape and structure of ears remain intact for years and are again a unique body feature.

静脉识别：使用光学扫描仪扫描手掌，手指或眼睛的静脉，并将静脉图案存储为唯一的标识符。

Odor recognition: This involves scent recognition using unique chemical patterns.

后识别：这正在开发技术，该技术可以测量一个人在椅子上的轮廓和压力点，以进行唯一的识别。它正在发展为汽车的防盗技术。

Behavioral identifiers:
Signature recognition: This involves identification of handwriting in the signatures. This technology is widely used for authentication in banking and financial transactions.

Voice recognition: This uses the voice pattern of a person as a unique identifier.

步态识别：这涉及提取步态特征以识别。

Gesture recognition: This involves the identification of gesture patterns for personal identification.

Keystroke recognition: This involves measuring a person’s key-down and key-up patterns on a keyboard and using it for identification.

社交媒体接触识别:这个网络curity system tries to identify an online registered user based on her past interaction and engagement with a website.

Advantages and disadvantages of biometrics
生物识别系统具有多个优点。这些是一个人独有的，并且不会在一生中改变。这些是不可转让的，几乎不可能伪造或模仿。窃取生物特征数据也不容易。因此，生物识别安全系统是最可靠和高效的。也有一些缺点。生物识别安全系统通常需要更昂贵的基础架构。即使不容易模仿或伪造的生物识别数据，也可以在本地或集中式服务器上存储。如果生物识别系统未获得足够的数据，则无效。即使适当地存储生物特征识别，仍然很少有误报和假否定的机会。

生物识别技术的应用
At present, biometrics is widely used in forensics, law enforcement, airport security, healthcare, military and intelligence, civil identification, civil security, immigration control, access and authentication, banking, authentication of financial transactions, point-to-point sales, and anti-theft technologies. The biometric data has even greater scope when combined with other useful datasets.

Challenges in biometrics
Biometric scanners are integrated into embedded devices as well as in smartphones and many consumer devices. Many biometric identification systems such as face recognition, voice recognition, and gesture recognition can be easily implemented online without any specific infrastructure. The increasing presence of biometric scanners, surveillance cameras, and their connectivity to online networks has raised concerns about data security, privacy infringement, identity protection, and device safety.

Conclusion
Biometric sensors are gaining ground, particularly in security and surveillance systems. They are at least more reliable and efficient than traditional password or PIN-based security systems. Apart from security applications, biometric data has its scope when combined with demographic, geographical, medical, scientific, financial, and economic datasets.

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